Financial markets are dynamic systems with complex interactions,
which share common features in various aspects with those in traditional physics. In recent years, large amounts of historical data have piled up in
stock markets. This allows a quantitative analysis of the financial
dynamics with the concepts and methods in statistical mechanics, and many empirical results have been documented
[1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}.
Very recently, these results are also supported and augmented with the online big data.
For examples, the price change may be predicted with the mood on Twitter [25]}, and the trading behavior may be quantified with
the Google search volume and Wikipedia topic view times [26]}, [27]}.
Statistical properties of the price fluctuations and cross-correlations
between individual stocks attract much attention, not only scientifically for
unveiling the complex structure and internal interactions of the
financial system, but also practically for the asset allocation
and portfolio risk estimation [28]}, [5]}.
